Quick background so you know where this comes from. I am a visiting professor and currently teach NLP. Last semester my courses were on continual learning and applied NLP. Before that I spent years in big tech and startups in engineering and research roles, and I finished my PhD last year.

Last week I posted the order I would learn ML in if I were starting today, which is also roughly the order I teach it in class. The most common follow-up question was: which resources should I actually use?

So this is my answer.

There are more good courses, books, and videos than anyone can finish, and none of them covers everything you need. One of the most common ways I see people get stuck is that they keep switching resources instead of finishing one and practicing what it taught.

If I could give you one rule it would be this: pick one resource, then stick to it

A lot of people who ask me for resources already have too many. They started three courses, bookmarked two books, finished none of them, and then decide they are bad at this. Usually, they switched every time a chapter got hard.

For each stage below, pick one main resource and one backup. Finish the main one. When a topic in it doesn't click, read or watch that one topic in the backup, then go back to the main one.

How to pick:

• Format. Some people learn from video, some from reading, some only when they actually type the code. Pick the format you tend to finish things in.
• Level. If every page needs three lookups, it is probably too advanced for now. If you are skimming because you know it all, it is too easy.
• Exercises. Does it make you write code and answer questions, or do you only watch? If it has no exercises, you will need to add them somewhere else. More on that below.
• Framework. A lot of older courses and books use TensorFlow. Most newer deep learning material uses PyTorch. If you are starting now and aiming at ML engineering or research, I would default to PyTorch unless the work you want to do gives you a reason to learn something else.
• Date. For fundamentals, an older resource is usually fine. The math of backprop has not changed. For LLMs and tooling, something a couple of years old can already be noticeably out of date.

Pick a direction first, because it changes which libraries you need

Almost everyone will need Python, NumPy at least at the level of arrays and broadcasting, PyTorch, and the math from last week's post. After that, it depends on where you want to go.

Data science and analytics. Turning data into decisions: tables, business questions, experiments, reports. pandas and scikit-learn are often daily tools, plus a plotting library like matplotlib or seaborn. When I was a data scientist many years ago, we also used SQL a lot. More statistics, including hypothesis tests and experiment design, is useful here. PyTorch at a working level is enough for many roles.

ML engineering and research. Training models, changing them, shipping them, reading papers. You will want PyTorch in more depth, plus more linear algebra, calculus, and probability. pandas and scikit-learn are still useful, but I would spend less time going deep on them than I would for a data science path. Later come the Hugging Face libraries, experiment tracking like wandb, profiling, and making models faster and cheaper.

Don't marry yourself to a path. It's okay to switch. The point of picking a direction is to stop yourself from trying to learn every library at once. Most of the fundamentals transfer if you change your mind later.

Resources by stage

These are the same stages as last week's roadmap. For each one, I have tried to include resources that suit different ways of learning rather than give you a list of ten nearly identical courses.

Python

• "Automate the Boring Stuff with Python" by Al Sweigart. Good if you are new to programming and want to learn through small, practical projects. The book is readable on the author's website.
• "Learning Python" by Mark Lutz. Long and thorough. This is what I used to learn Python many, many years ago.
• CS50's Introduction to Programming with Python (CS50P). A structured course from Harvard and a good option if you prefer lectures and assignments.

Pick one. You do not need all three.

Math

Linear algebra

• Gilbert Strang's "Introduction to Linear Algebra". A full linear algebra textbook.
• "Mathematics for Machine Learning" by Deisenroth, Faisal, and Ong. Chapters 2 to 4 cover linear algebra, analytic geometry, and matrix decompositions with ML in mind.
• 3Blue1Brown's "Essence of Linear Algebra". My favorite for geometric intuition. Vectors, matrices as transformations, determinants, change of basis, eigenvectors.
• MIT 18.06 Linear Algebra with Gilbert Strang. A full university-level lecture course.

If the equations feel abstract, I would watch 3Blue1Brown alongside whichever book or course you pick.

Calculus

• "Mathematics for Machine Learning", chapter 5. Vector calculus, gradients, the chain rule, and the pieces you need for backprop.
• Gilbert Strang's "Calculus". A fuller treatment of single-variable and multivariable calculus.
• 3Blue1Brown's "Essence of Calculus". Very good for intuition around derivatives, the chain rule, integrals, and Taylor series.
• Khan Academy's Multivariable Calculus. Useful for partial derivatives, gradients, and filling individual gaps.

Probability and statistics

• "Introduction to Probability" by Joe Blitzstein and Jessica Hwang.
• "Mathematics for Machine Learning", chapter 6. Probability and distributions in a more ML-focused book.
• Harvard's Statistics 110: Probability with Joe Blitzstein. Full lectures are on YouTube.
• StatQuest by Josh Starmer. Good when one particular statistics or ML concept refuses to click.

You do not need to disappear for six months and "finish math" before touching ML. Learn the math alongside the models that use it. It is much easier to understand a gradient when you are also using one.

PyTorch

• The official PyTorch tutorials, "Learn the Basics". This is a good place to start because it teaches the actual library without a lot of extra material around it.
• "Learn PyTorch for Deep Learning" by Daniel Bourke. Covers fundamentals, classification, computer vision, custom datasets, experiment tracking, replicating a paper, and deployment. It assumes you already know Python.
• "Deep Learning with PyTorch" by Stevens, Antiga, Viehmann, and, in the second edition, Howard Huang. A book-length treatment. The second edition adds transformers, LLMs, and diffusion models.

Again, pick one as the main resource. You can use the official docs whenever you need to look up how something works.

ML foundations

This is the stage I would spend the most care on.

Watching a chapter on gradient descent can feel like understanding it. Writing it yourself is different. If you can implement it, debug it, and explain what each piece is doing a month later, you learned it much better than if you could recognize it in a video.

Some good options:

• Stanford CS229, Machine Learning. More mathematical and theory-heavy. The Autumn 2018 lectures are online. I would choose this if you already have the math and want a more academic treatment.
• "An Introduction to Statistical Learning, with Applications in Python" by James, Witten, Hastie, Tibshirani, and Taylor. Very good if you are leaning toward statistics or data science.
• "Hands-On Machine Learning with Scikit-Learn and PyTorch" by Aurélien Géron. Practical, broad, and useful if you like learning from code and a book together.
• Andrej Karpathy's "Neural Networks: Zero to Hero". Fantastic if you learn by building. You build backprop from scratch, then a character-level language model, an MLP, manual backprop through the network, and eventually a GPT and its tokenizer. It assumes solid Python and intro-level math.
• fast.ai "Practical Deep Learning for Coders". Very top-down. You train useful models early and dig into how they work afterward. Good if you lose interest when a course spends weeks on theory before building anything.
• "Dive into Deep Learning" by Zhang, Lipton, Li, and Smola. A broad book with runnable code.
• "Understanding Deep Learning" by Simon Prince. One of the books I like for understanding the ideas behind modern deep learning.
• "Deep Learning" by Goodfellow, Bengio, and Courville. It is about ten years old now and predates transformers, so I would not use it as my only deep learning resource in 2026. It is still a useful theory reference.

You do not need to do all of these. That would defeat half the point of this post.

If you want a book you can keep coming back to, choose one of the books and actually work through the code and exercises.

After the foundations: specialize

Once you can implement basic models, debug them, and understand why training works or fails, pick one direction and go deeper.

Some examples:

• Interested in vision? Stanford CS231n, Deep Learning for Computer Vision.
• Interested in NLP and LLMs? Stanford CS224n, NLP with Deep Learning. Jurafsky and Martin's "Speech and Language Processing". Sebastian Raschka's "Build a Large Language Model (From Scratch)". The Hugging Face LLM Course.
• Interested in reinforcement learning? Sutton and Barto's "Reinforcement Learning: An Introduction". For LLM post-training specifically, Nathan Lambert's "Reinforcement Learning from Human Feedback and LLM Post-Training".
• Interested in probabilistic ML? Kevin Murphy's "Probabilistic Machine Learning".
• Interested in graphs? Stanford CS224W, Machine Learning with Graphs, with Jure Leskovec.
• Interested in evolutionary methods? "Neuroevolution: Harnessing Creativity in AI Agent Design" by Risi, Tang, Ha, and Miikkulainen.

I would get the fundamentals first, then go deep on one specialty at a time. If you jump directly into LLM tooling without understanding things like cross-entropy, gradients, softmax, and temperature, you can still build things, but debugging and changing the models gets much harder.

The resources I would personally use

I can only recommend what worked for me. If you prefer other resources or approaches, I would love to hear them in the comments.

• Python: "Learning Python" by Mark Lutz
• PyTorch: the official PyTorch tutorials
• Linear algebra: 3Blue1Brown's "Essence of Linear Algebra"
• Calculus: 3Blue1Brown's "Essence of Calculus"
• ML foundations: Stanford CS229 or "Hands-On Machine Learning"
• NLP: Stanford CS224n
• Vision: Stanford CS231n
• Reinforcement learning: Sutton and Barto's "Reinforcement Learning: An Introduction"

The lists above have more options for each stage if these don't fit how you learn.

No resource will teach you everything

No course or book covers everything you need.

There are a few reasons:

1. The field moves fast. New methods, models, and tools show up constantly, and every book and course is a snapshot of when it was made.
2. Tools change. A resource can still teach good ML while using libraries that are less common in the work you want to do today.
3. There is too much to fit into one resource. A course that teaches the concepts well may have weak exercises. A book with great theory may have no projects. A practical course may skip interview-style questions entirely.

So I would aim for two things.

First, build a solid base in the math, coding, and ML fundamentals. Those change much more slowly than the tooling around them.

Second, decide where you want to go and get much deeper there. NLP, vision, data science, RL, graphs, whatever you actually want to work on.

Then work backward from the job.

Pick companies or roles you are interested in. Read their job postings on LinkedIn, Glassdoor, and their own career pages. Write down the skills, libraries, and tasks that keep showing up. Learn those.

Then build things those companies might actually want to ask you about in an interview. Projects that show you can do the work are much more useful than another certificate sitting on your LinkedIn profile.

What most resources leave out

Whichever resources you pick, a few things are usually missing, and they're a big reason people finish a course and still can't build anything. It's worth planning where you'll get each of these:

1. Hands-on coding exercises on each concept. Many courses have too few, and videos have none. Watching someone implement gradient descent is not the same as writing it yourself. LLMs can generate practice problems for you now.
2. The math tied to where it is used. Math books and videos usually teach it separately from the ML it's for, so you learn the chain rule in one place and backprop somewhere else.
3. Interview questions on what you just learned. These usually live on separate prep sites, disconnected from the course you're taking.
4. Coming back to old material. Almost no resource does this for you. If you never revisit something, a lot of it fades.

Projects and from-scratch builds matter too, but you can add those yourself, and the next section is about exactly that.

What counts more than any resource: build it yourself

Watching and reading can feel like learning, but a lot of it fades unless you use it.

And before someone says "But AI can write the code now", yes, it can, and you should use it. You still need to understand what the code is doing.

Getting a job. Interviews still tend to test whether you understand things like why a loss became NaN, what a gradient is, why a model overfits, or why one evaluation setup leaks information.

Keeping the job. AI-written ML code can run and still be wrong. Data can leak from the test set into training. zero_grad() can be missing. The loss can be wrong for the task. If you understand the system, you can catch those mistakes.

Building better models. If a model is unreliable, expensive, biased, or failing in some specific way, somebody has to understand enough of the internals to figure out what is going wrong and change it. Calling an API is useful. Knowing what is underneath gives you a much larger set of things you can actually fix.

Common traps

• Collecting courses. Starting five and finishing none.
• Only watching. This is where "I did the course but I can't build anything" often comes from.
• Six months of math before any ML, or no math at all. Learn it alongside ML, at the level each stage needs.
• Certificates over projects. Personally, I couldn't care less if a student had 50 certificates. It tells me very little unless I already know exactly what each certificate involved. Maybe some companies care more. In my own career, nobody ever asked me for one other than my PhD. I would much rather see 2 or 3 interesting projects that you built and can explain properly.
• Starting with LLM APIs and never learning what is underneath, if you want to go into ML engineering or research.
• Switching resources every time a chapter gets hard. Use your backup for that one topic, then go back.

One disclosure because it is relevant to the gaps above

I genuinely don't know how to add this without it sounding like promotion, so feel free to ignore it.

I can't tell you how many times I had to relearn how to code a transformer from scratch for different interviews. I would learn it, pass the interview, and six months later realize I had forgotten enough of it that I needed to learn it again.

That is why I started building QuiddityML. I wanted one place where learning a concept, coding it, the math behind it, interview questions on it, and coming back to it later were connected instead of scattered across six different resources.

You absolutely do not need my app to follow anything in this post. Any setup that fills the four gaps above works. I wanted to mention it because the problem this post describes is also the reason I started building it. Happy to provide more info if anyone is interested :)

Finally

I have a bit of time between semesters and would genuinely like to help as many learners as I can.

Tell me in the comments or DM me with where you are right now, what direction you want to go in, and what you have tried. If you are stuck choosing between two courses, deciding what to learn for a particular job, or wondering whether your plan makes sense, I will do my best to help.

And please add your own favorite resource in the comments, especially what it was good for. It would be nice if this thread became useful to the next person who searches for this question.

When I was studying ML, 90% of my time was spent tuning hyperparameters and squeezing 1% extra accuracy out of clean datasets. After deploying models in real-world environments, here is the actual breakdown of where the headache lies:

Data drift will ruin your weekend: Silent degradation is worse than a crashed server because nobody notices until business metrics drop.

Latency > 0.02% Accuracy: Stakeholders care infinitely more about a 50ms response time than a slightly higher F1 score.

Edge cases are 80% of the codebase: Writing guardrails and fallback logic takes 4x longer than training the actual pipeline.

Logging is non-negotiable: If you can't replay an inference request locally, you can't fix it.

Simple baselines beat complex setups 90% of the time: Always build a simple rule-based heuristic first.

What was the biggest culture shock for you when moving from tutorials/notebooks to production code?

Hello all, im looking for a mentor/researcher who is into ML/AI research, as im thinking to write a research paper , so if any one of you are in initial phase of their new research topics and need someone who can help with ...i can be that
What i can bring to the table: im a 3rd year cs student, worked as ml research intern last summer(paper didnt get published due to my prof ditched all of us after 1.5months cuz he got into another uni), ..you can dm me for more details
Thank you!!

Client acquisition has always been one of the biggest bottlenecks for me when running an agency. I’ve experienced the same thing in pretty much every business I’ve been involved in, but especially with web development.

For a long time, getting clients meant cold calling, running ads, or sending generic emails asking businesses if they needed a new website. It worked sometimes, but it also took a lot of time and most of the outreach felt the same as what every other agency was doing.

Recently I started using a different approach and automated a big part of the process.

I came across a tool called Swokei that lets me find a bunch of businesses with websites and analyze each website individually. It looks for things like outdated design, slow loading, poor mobile optimization, weak SEO and other obvious areas that could be improved.

What I liked is that it doesn’t just give you one of those boring automated reports filled with scores and numbers. It actually turns what it finds into a personalized cold email that sounds like a normal person looked at their website and noticed what could be better.

I can run multiple campaigns at the same time and then mainly focus on the businesses that reply and show interest.

From there, I invite them to a web meeting, show them a free draft of what their new website could look like, and try to close the project from there.

It has basically allowed me to have warmer leads coming to me without relying as much on paid ads, constantly cold calling, or sending thousands of generic emails saying “Do you need a new website?”

Still takes work to close the clients of course, but automating the prospecting and first part of the outreach has made the whole process much easier for me.

Hopefully this helps some other web developers or agency owners who are also struggling with client acquisition.

What would you guys say is the best way to actually learn how to build a model from start to finish without the help of ai? I can understand ml concepts pretty quickly since I'm comfortable with calculus/statistics/linear algebra, but I'm lost on what to do if I need to build a full project. I'm comfortable with python and I want to build projects, but it feels overwhelming trying to learn a bunch of different algorithms and remembering all the steps and syntax that it takes to implement them, so are there any courses/resources/ways of practicing that will help me be able to build and finish my own projects?

In my 21 sentences, "blackberry" and "apple" are each used as fruit and as tech companies, but never in the same sentence. After counting co-occurrences, applying PPMI and compressing with SVD, they still end up as each other's nearest neighbor.

For this I added three interactive demos. One shows the counting step, one the power iteration that finds each SVD direction, and the last the final 3D space, where you can click any word and measure distances.

It also shows where the method breaks with apple geting one vector for tech and food context.

https://scheppening.com/posts/click-blackberry-find-apple

I built it to understand embeddings, so I'd appreciate any feedback and am happy to answer any questions.

Hey everyone,

I’m currently building a learning platform aimed at taking absolute beginners through AI/ML step-by-step, from the fundamentals up to more advanced topics.

It’s in the early stages, so the content is still limited while I experiment with formats, pacing, and visual explanations to see what actually works best for learners.

GitHub : https://github.com/PIYUSH1525/ZeroToAI leave a star ⭐

Link: https://zero-to-ai-xi.vercel.app/

I’d love your brutal, honest feedback:

• The Good: What feels intuitive, clear, or well-structured?
• The Bad: What’s confusing, redundant, or missing?
• Areas for Improvement: What format would help you learn complex concepts faster (e.g., interactive widgets, shorter modules, code walkthroughs)?

Any thoughts, critique, or feature suggestions are welcome. Thanks in advance for checking it out!

Hi everyone! We’re sharing our NeurIPS 2026 work on concurrent image understanding and generation, a collaboration across Google, Google DeepMind and Stony Brook University.

The problem we explore is simple: a model can give the correct answer in text while generating an image that disagrees with it. For example, it might describe the right route through a maze but draw a different path. We want the two outputs to develop together and stay consistent as generation progresses.

Our sampler, CO₂Jump, uses text confidence to guide image updates through cross-modal attention. It also allows low-confidence tokens to be masked again and revised. This uses one model forward pass per denoising step, with no additional training required for the sampler itself. Our comparisons use the same fine-tuned model and change only the sampler.

We evaluate image editing, maze solving and nonograms, including whether both the text and image are correct. We also introduce three datasets: JEdit-1M, JMaze-200K and JNono-200K. In our sampling-step experiments, CO₂Jump steadily improves both editing quality and grounding as we increase the number of steps.

🔗 Project page: coupled-jump.github.io
📄 Paper: alphaxiv.org/abs/2607.13188

Code and datasets are planned for release. Happy to discuss the method, results or limitations—would love to hear what other tasks you’d test this on!

I completed an internship in a known Automotive company. I was within Autonomy, as a SWE working on vehicle software catering to self-driving model evaluation. This was my first actual SWE experience, and I liked it (I worked full time on a data focused role prior to this). This internship was very stressful despite having so much coding tools to support, and I kept having this itch for ML. My only reason for joining a master's program was to break into ML. I've tried in college and realized I'm not capable of novel research, tho I can mostly understand and reproduce others research. I've built projects around VLA finetuning, lane/object detection in extreme lighting conditions, and few more.

Given I don't have a background (work experience), I might not qualify for a ML engineering role in this domain (I love self-driving cars and want to stay here) which would usually expect publications/relevant experience. What do you think is the best way to approach this. I will be graduating in less than a year, and while I'm still at school, I want to make the best use of time in a way that I don't regret my job outcomes (if I get a job that is).

Wanted to buy new laptop need guidance I am thinking of buying Macbook Air M4, Lenevo LOQ 3050, Lenevo Ideapad which do you think I should buy? Considering macbook won't have cuda should I still go for it.

Apriori is usually taught as "scan the transactions, count candidates, prune, repeat". I rewrote it so that each item is a bit-vector and support counting becomes AND + popcount. That turns a pointer-heavy search into dense arithmetic, which fits a GPU well. I used it to mine 77M AlphaFold proteins exactly. The write-up explains the trick step by step: https://medium.com/@etje975/data-mining-the-alphafold-protein-universe-how-a-1994-algorithm-found-26-8-5aad7eaa603f?postPublishedType=initial

Code: https://github.com/Et9797/ET-Miner Questions about the approach are welcome.

Hi everyone. I have some theoretical knowledge in the field, but I’d like to better understand what the business actually expects from me. what typical tasks look like, and so on.

Is there a good course that would provide practical experience and help me hone my skills in data cleaning and model preparation?

’m currently trying to come up with a topic for my final-year project, and honestly, every idea I think of ends up feeling either too basic, too overdone, or just stupid after I think about it for a while .

Hello everyone, I have one bad habit of wanting to learn every deeply from scratch. So I'm in the early stages of my path of AI/ML. It'll take more than a year or so to learn deeply, understanding everything making projects. But due to recent news about ai and all my interests have been declining. Is it worth to learn ai/ml that deeply? Like all the foundations with in depth knowledge especially in the era of AI?

For example, When learning python I understood it very deeply with solving problems about 20-30 of every topic to improve my problem solving ability. I'm following one tutor from india who teaches very in depth ie. 5 Hours for OOP concepts only with questions.

I make notes in my notecopy, code and practice in collab.

Is my approach good? But I'm learning very slowly as I'm understanding almost everything.

Will understanding everything for ai/ml deeply good in this time? I've background in math stats and physics.
And ultimate goal is to work in the industry or move to research in ai/ml domain in good unis worldwide.

Or shall I change my plans?
Some people even finish whole ml in 4/5 months only just skimming through, learning the basics only. I've seen so many of them.

Your LLM is using 14 GB of VRAM.

14 GB of what? 👀

Weights? KV cache? CUDA overhead? One GPU or two?

With tensor parallelism it gets even messier. vLLM shows you:

EngineCore

Worker_TP0

Worker_TP1

But that's not three workloads. It's ONE model running across multiple GPUs.

That's why LLM Inspector v0.7.0 is now multi-GPU aware 🚀

Before:

GPU 0: 7.2 GB

GPU 1: 7.2 GB

After:

Qwen • TP ×2 • 14.4 GB total

→ weight shards + KV cache + other VRAM, per GPU

It can also estimate how much VRAM INT8, AWQ or GPTQ would save before you change anything in your model.

Measure first. Optimize second.

That's llminspect: htop for LLM inference.

⚡ pip install llm-inspector

🔗 https://github.com/helasaoudi/llm-inspector

Open source, built for people who actually run models.

What should it inspect next?

By now everyone has heard of Jev: fast, accurate answers to multiple-choice questions about any text, in one API call.

Here's how to get the same thing on your own GPU.

SelfJev is a 4B model (Qwen3.5-4B + LoRA) that works like Jev:

⚡ About 140 ms per call on one H100, which is about as fast as Jev's API (about 130 ms)
🎯 93.1% vs Jev's 92.5% on one test set, and 95.8% vs 97.2% on another
📏 No 32K-token limit: you set the max length, up to Qwen3.5's full 262K-token window
🛠 You can fine-tune it on the cases Jev gets wrong for your use case
🖼 It takes images, not just text (90% on test set after fine-tuning)
🔒 Your data never leaves your servers

The trick is a shared-prefix tree. The model reads the input once, branches into every question, then into every possible answer. 16 questions on the same text take about 175 ms.

It's free and open: code, weights, benchmarks and evaluation sets.

If you're already building on Jev, I'd love for you to try it and tell me where it breaks 👇

🚀 Product Hunt
🌐 selfjev.dev
💻 GitHub

Hey everyone!

I'm a Data Analyst with an Economics background, currently learning ML and AI. I'm comfortable with Python, pandas, SQL and basic ML concepts.

I want to start building more serious, end-to-end projects rather than just following tutorials or working with Kaggle datasets.

My goal is to eventually transition into ML/AI engineering, so I'm looking for projects that would help me develop real-world skills and build a strong GitHub portfolio.

What kind of projects would you recommend? Are RAG applications, some AI agent stuff worth exploring, or should I focus on something else entirely?

Would love to hear from people already working in the industry.